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PubMed1999

Using sensitivity analysis for efficient quantification of a belief network.

Sensitivity analysis is a method to investigate the effects of varying a model's parameters on its predictions. It was recently suggested as a suitable means to facilitate quantifying the joint probability distribution of a Bayesian belief network. This article presents practical experience with performing sensitivity analyses on a belief network in the field of medical prognosis and treatment planning. Three network quantifications with different levels of informedness were constructed. Two poorly-informed quantifications were improved by replacing the most influential parameters with the corresponding parameter estimates from the well-informed network quantification; these influential parameters were found by performing one-way sensitivity analyses. Subsequently, the results of the replacements were investigated by comparing network predictions. It was found that it may be sufficient to gather a limited number of highly-informed network parameters to obtain a satisfying network quantification. It is therefore concluded that sensitivity analysis can be used to improve the efficiency of quantifying a belief network.

Algorithms↗
PubMed2001

Sensitivity analysis and calibration of the parameters of ESWAT: application to the River Dender.

The paper deals with the sensitivity analysis and parameter calibration of a complex river water quality model, implemented in ESWAT. The Extended SWAT includes a QUALIIE-based river quality simulator, in view of an integrated analysis of water quantity and quality management practises. The sensitivity analysis uses Latin Hypercube Sampling and criteria related to the duration of low concentrations of dissolved oxygen and the occurrence of high algae concentrations. The analysis on the river Dender shows that parameters related to the growth and die-off of the algae have the largest impact, while also the BOD decay constant and the benthic oxygen demand are important. A subsequent calibration of these most important parameters shows however that the optimal values of the parameters related to the activity of the algae are statistically not significant. This apparent contradiction is due to the poor information content of the measurements. It is concluded that the application illustrates the complementarity of the sensitivity analysis and the parameter calibration.

Belgium↗
PubMed2001

Addressing uncertainty in medical cost-effectiveness analysis implications of expected utility maximization for methods to perform sensitivity analysis and the use of cost-effectiveness analysis to set priorities for medical research.

This paper examines the objectives for performing sensitivity analysis in medical cost-effectiveness analysis and the implications of expected utility maximization for methods to perform such analyses. The analysis suggests specific approaches for optimal decision making under uncertainty and specifying such decisions for subgroups based on the ratio of expected costs to expected benefits, and for valuing research using value of information calculations. Though ideal value of information calculations may be difficult, certain approaches with less stringent data requirements may bound the value of information. These approaches suggest methods by which the vast cost-effectiveness literature may help inform priorities for medical research.

Cost-Benefit Analysis↗
PubMed1982

Sensitivity analysis of biological models.

An inhomogenous linear model of the lung mechanics system was selected for the demonstration of one of the methods of sensitivity analysis. Given the values of state variables, the sensitivity of the model makes possible a safe adjustment of coefficients, without leading to large errors of solution with even a small deviation in adjustment. Any mathematical model only represents a picture of basic and substantial dynamic properties and relations of a real object. By means of sensitivity analysis it is possible to obtain a faithful description of the real object's behavior by computing the simplest model solution, with knowing at the same time, by sensitivity analysis, the range of errors introduced by simplifications and approximations.

Computers↗
PubMed1997

Sensitivity analysis in health economic and pharmacoeconomic studies. An appraisal of the literature.

The objective of this study was to analyse the extent of reporting of sensitivity analyses in the health economics, medical and pharmacy literature between journal types and over time. 90 articles were chosen from each of the bodies of literature on health economics, medicine and pharmacy. MEDLINE, EMBASE and International Pharmaceutical Abstracts were searched for English-language economic studies published between 1989 and 1993. The studies chosen for inclusion had to be original articles published in one of the selected journals between January 1989 and December 1993, involving a comparison between drugs, treatments or services, and evaluating both costs and outcomes. 123 articles initially met these criteria; however, 16 were inappropriate, 17 were randomised out, leaving 90 studies (73%) that were used (30 from each literature group). Data were extracted independently by 5 raters using a validated checklist. Inter-rater reliability was assessed by calculating kappa. 53 of the 90 articles (59%) conducted sensitivity analyses. 39 (74%) stated explicitly that a sensitivity analysis was being performed; this was noted in the Methods section of 35 papers (67%). 80% of health economics journals, 70% of medical journals and 20% of pharmacy journals conducted sensitivity analyses. Despite the fact that all published pharmacoeconomic guidelines suggest the use of sensitivity analysis, only 59% of studies between 1989 and 1993 did so. Improvement is required, especially in the pharmacy literature. No time trends in the conduct of sensitivity analyses were detected. However, the sample may not have been sufficient to detect such trends. Pharmacoeconomic guidelines should provide more details on preferred methods of sensitivity analysis and on desired parameters.

Economics, Pharmaceutical↗
PubMed1989

Sensitivity analysis for GMDH correction modeling of MKG signals.

The purpose of this paper is to propose application of sensitivity analysis to the GMDH modeling for the correction of distorted kinesiographic signals recorded for inter-lattice points in space, and to evaluate its correction accuracy to estimate coordinates of an inter-lattice point, i.e., an observation, distorted coordinates of a nominal lattice point which is most adjacent to a given inter-lattice point is searched. Variations, i. e., differences, between distorted coordinates of the observations and their concomitant nominals are calculated. Instead of substituting kinesiographic measurements of the nominal and its neighbouring eight lattice points, the sum of the observation and its corresponding variation is substituted into the GMDH model which has been employed for the correction of distorted measurements of the aforementioned lattice point. A stereotaxic device is developed to stimulate mandibular jaw movement and determine position of a magnet transducer of a mandibular kinesiograph (MKG). Nominals of 3-D coordinates of the inter-lattice points are determined by the stimulator together with simultaneous recording of distorted output signals from the MKG which correspond to the nominals. Distorted signals are corrected on the basis of the sensitivity-oriented correction modeling. A mean estimation error of 0.16 mm (s. d., 0.19 mm) is determined for 24 inter-lattice coordinates. Thus, the application of sensitivity analysis to the GMDH modeling is confirmed to be effective.

Humans↗
PubMed2000

Sensitivity analysis in quantitative microbial risk assessment.

The occurrence of foodborne disease remains a widespread problem in both the developing and the developed world. A systematic and quantitative evaluation of food safety is important to control the risk of foodborne diseases. World-wide, many initiatives are being taken to develop quantitative risk analysis. However, the quantitative evaluation of food safety in all its aspects is very complex, especially since in many cases specific parameter values are not available. Often many variables have large statistical variability while the quantitative effect of various phenomena is unknown. Therefore, sensitivity analysis can be a useful tool to determine the main risk-determining phenomena, as well as the aspects that mainly determine the inaccuracy in the risk estimate. This paper presents three stages of sensitivity analysis. First, deterministic analysis selects the most relevant determinants for risk. Overlooking of exceptional, but relevant cases is prevented by a second, worst-case analysis. This analysis finds relevant process steps in worst-case situations, and shows the relevance of variations of factors for risk. The third, stochastic analysis, studies the effects of variations of factors for the variability of risk estimates. Care must be taken that the assumptions made as well as the results are clearly communicated. Stochastic risk estimates are, like deterministic ones, just as good (or bad) as the available data, and the stochastic analysis must not be used to mask lack of information. Sensitivity analysis is a valuable tool in quantitative risk assessment by determining critical aspects and effects of variations.

Animals↗
PubMed1987

Spreadsheets simplify sensitivity analysis for capital decisions.

The availability and ease of use of electronic spreadsheets removes the previously cumbersome number crunching burden of sensitivity analysis. In this article, the advantages of sensitivity analysis will be reintroduced and a model will be presented using a pro forma statement for a freestanding magnetic resonance imaging center.

Capital Expenditures↗
PubMed1998

Malononitrile as a new derivatizing reagent for high-sensitivity analysis of oligosaccharides by electrospray ionization mass spectrometry.

A new method for the high-sensitivity analysis of oligosaccharides by negative ion electrospray ionization mass spectrometry was developed through a chemical derivatization of oligosaccharides. Oligosaccharides were derivatized to dinitrile compounds from the reaction with malononitrile under mildly basic conditions. The derivative of maltoheptaose was detected mainly as the [M-2H]2- ion in negative ion mode with 20 fmol sensitivity, even in unpurified samples. In this malononitrile derivatization method, no inorganic reagent, other than sodium hydroxide as a base catalyst, is used. Also, because excess ligand (malononitrile) is volatile, high sensitivity detection is realized without any solvent extraction or chromatographic purification. The detection limit can also be decreased by simple on-line cartridge filtration to 200 attomol which is 10(5) times better than that of free maltoheptaose. Structural information for oligosaccharide derivatives was obtained by collision induced dissociation. This malononitrile derivatization method is convenient and efficient for the sensitive analysis of oligosaccharides.

Carbohydrate Sequence↗
PubMed1984

Design sensitivity analysis: a new method for implant design and a comparison with parametric finite element analysis.

A unified theory of structural design sensitivity is proposed to be used in conjunction with the parametric design variation method traditionally used in finite element analyses applied to biomechanics problems. Bone cement strain energy density dependence on cement and stem modulii of elasticity as analyzed with the theory of structural design sensitivity analysis is compared parametrically varied finite element results. Two-dimensional, eight-noded isoparametric and interface finite elements with optimal stresses at Gauss points are employed. Design sensitivity for strain energy density compares well with perturbation of design and reanalysis by finite element techniques.

Hip Prosthesis↗
PubMed1999

Estimating uncertainty ranges for costs by the bootstrap procedure combined with probabilistic sensitivity analysis.

When an economic evaluation incorporates patient-level data, there are two types of uncertainty over the results: uncertainty due to variation in the sampled data, and uncertainty over the choice of modelling parameters and assumptions. Previously statistical methods have been used to estimate the extent of the former, and sensitivity analysis to estimate the extent of the latter. Ideally interval estimates for economic variables should reflect both types of uncertainty. This paper describes a method for combining bootstrapping with probabilistic sensitivity analysis to estimate a total 'uncertainty range' for incremental costs. The approach is illustrated using cost data from a randomized controlled trial of endoscopy for Helicobactor pylori negative young dyspeptic patients. The trial failed to demonstrate any clinical benefit from endoscopy, which was on average pound 395 more costly. The combined 95% uncertainty range for incremental costs (-pound 236 to pound 931) was wider than 95% intervals estimated by either probabilistic sensitivity analysis (pound 43 to pound 592) or the non-parametric bootstrap method (-pound 95 to pound 667) alone. The method can easily be extended to the calculation of uncertainty ranges for incremental cost-effectiveness ratios.

Confidence Intervals↗
PubMed1995

A comparison of sensitivity analysis techniques.

Modeling the movement and consequence of radioactive pollutants is critical for environmental protection and control of nuclear facilities. Sensitivity analysis is an integral part of model development and involves analytical examination of input parameters to aid in model validation and provide guidance for future research. Sensitivities of 21 input parameters have been analyzed for a specific-activity tritium dose model using fourteen methods of parameter sensitivity analysis. This report demonstrates, for each sensitivity method, the required calculational effort, the sensitivity ranking of parameters, and the relative method performance. The sensitivity measures include the following: partial derivatives, variation of inputs by 1 standard deviation (SD) and by 20%, a sensitivity index, an importance index, a relative deviation of the output distribution, a relative deviation ratio, partial rank correlation coefficients, standardized regression coefficients, rank regression coefficients, the Smirnov test, the Cramer-von Mises test, the Mann-Whitney test, and the squared-ranks test.

Diet↗
PubMed2000

A sensitivity analysis to separate bias due to confounding from bias due to predicting misclassification by a variable that does both.

Variables that predict misclassification of exposure, outcome, or a confounder cannot be controlled by techniques that adjust for predictors of risk. They must be controlled by external adjustments. We confronted an analysis in which a variable predicted misclassification of the exposure and of a confounder. The same variable confounded the exposure-outcome relation. The analysis focused on the relation between less-than-definitive therapy and breast cancer mortality in the 5 years after diagnosis. Receipt of less-than-definitive prognostic evaluation predicted misclassification of definitive therapy (the exposure) and stage (a confounder). Prognostic evaluation also confounded the therapy-breast cancer mortality relation. We used a sensitivity analysis to separate the misclassification biases from the confounding bias. The relative hazard associated with less-than-definitive therapy in the original multivariable model equaled 1.75 (95% confidence interval = 1.02-3.00). The median estimate in 2,500 repetitions of the sensitivity analysis was a relative hazard of 1.64, and 90% of the estimates fell between 1.47 and 1.83. The sensitivity analysis suggests that less-than-definitive therapy confers an excess relative hazard of breast cancer mortality in the 5 years after diagnosis. The original analysis, which adjusted for confounding by prognostic evaluation but not its misclassification biases, overestimated the relative hazard.

Age Factors↗
PubMed1981

Sensitivity analysis and optimization for a head movement model.

A sixth order nonlinear model for horizontal head rotations in humans is analyzed using an extended parameter sensitivity analysis and a global optimization algorithm. The sensitivity analysis is used in both the direct sense, as a model fitting tool, and in the indirect sense, as a guide to experimental design. Resolution is defined in terms of the sensitivity table, and is used to interpret the sensitivity results. Using sensitivity analyses, the head and eye movement systems are compared and contrasted. Controller signal parameters are the most influential. Their variations and effects on head movement trajectories and accelerations are investigated, and the conclusions are compared with clinical neurological findings. The global optimization algorithm, in addition to automating the fitting of various types of data, is combined with time optimality theory to give theoretical time-optimal inputs to the model.

Computers↗
PubMed1994

Sensitivity analysis of relative accommodation and vergence.

A sensitivity analysis was performed to determine the variation in response to changes in parameter values of a previously developed nonlinear static model of accommodation and vergence. To determine normal behavior, model simulation responses were computed using previously obtained parameter values in 4 subjects under 2 conditions. In the first, relative accommodation was evaluated by maintaining the vergence stimulus constant at 2.5 meter angles (MA) and varying the accommodative stimulus from -2.5 to 2.5 diopters (D) in 0.25-D steps. In the second, relative vergence was evaluated by maintaining the accommodative stimulus constant at 2.5 D and varying the vergence stimulus from 25 prism diopters (PD) base-in to 25 PD base-out in 5-PD steps. Sensitivity of the model parameters, consisting of controller gains for accommodation (ACG) and vergence (VCG), crosslink gains for accommodation-to-vergence (AC) and vergence-to-accommodation (CA), deadspace operators for accommodation (AE +/- AD) and vergence (VE +/- VD), and the tonic levels for accommodation (ABIAS) and vergence (VBIAS) were assessed by varying them at 50% and 150% of their normal values. It was found that the accommodation and vergence systems were most sensitive to variation in crosslink gain, moderately sensitive to variation in controller gain and tonic level, and least sensitive to variation in size of the deadspace. These results may provide a quantitative basis for the occurrence of ocular dysfunctions associated with abnormal crosslink gains, such as strabismus, in clinic patients.

Accommodation, Ocular↗
PubMed1985

Probabilistic sensitivity analysis using Monte Carlo simulation. A practical approach.

The data for medical decision analyses are often unreliable. Traditional sensitivity analysis--varying one or more probability or utility estimates from baseline values to see if the optimal strategy changes--is cumbersome if more than two values are allowed to vary concurrently. This paper describes a practical method for probabilistic sensitivity analysis, in which uncertainties in all values are considered simultaneously. The uncertainty in each probability and utility is assumed to possess a probability distribution. For ease of application we have used a parametric model that permits each distribution to be specified by two values: the baseline estimate and a bound (upper or lower) of the 95 percent confidence interval. Following multiple simulations of the decision tree in which each probability and utility is randomly assigned a value within its distribution, the following results are recorded: (a) the mean and standard deviation of the expected utility of each strategy; (b) the frequency with which each strategy is optimal; (c) the frequency with which each strategy "buys" or "costs" a specified amount of utility relative to the remaining strategies. As illustrated by an application to a previously published decision analysis, this technique is easy to use and can be a valuable addition to the armamentarium of the decision analyst.

Decision Making↗
PubMed1997

Morbidity and mortality due to ascariasis: re-estimation and sensitivity analysis of global numbers at risk.

This paper presents estimates of the global numbers of people at risk from morbidity related to infection with Ascaris lumbricoides and the numbers of deaths from this infection. Morbidity is classified into 4 types: deficits in growth and fitness which are contemporaneous with infection, or permanent, overt acute illness of mild to moderate severity, and complications involving hospitalization. The estimation of morbidity is based on theoretical models of parasite distributions developed in previous papers. A sensitivity analysis is carried out in which parameters of the model are varied using a Latin hypercube sampling technique. The results estimate approximately 1300 million infections globally with 59 million at risk of some morbidity. The estimate for acute illness is 12 million cases per year with approximately 10,000 deaths. Most morbidity is in children. Sensitivity analysis suggests that infection estimates will not vary greatly with changes in parameter values but that morbidity estimates may be highly variable.

Adolescent↗